Systems and methods for calibrating well-completion techniques

ABSTRACT

A method for a completion operation of a well includes performing, by a simulator, an initial simulation based on geological data and an input parameter, the initial simulation providing simulated net pressure values for the well; receiving an indication of an actual net pressure value in the well; adjusting, by an RL agent, the input parameter to the simulator based on a difference between the actual net pressure value and a corresponding simulated net pressure value; performing an updated simulation based on the geological data and the adjusted input parameter, the updated simulation providing updated simulated net pressure values; iteratively adjusting the input parameter to the simulator, with the corresponding simulated net pressure value being from the updated simulated net pressure values; and providing an indication of an event at the well based on the actual net pressure value and the corresponding simulated net pressure value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of U.S. Provisional Application Ser. No.63/249,130 filed Sep. 28, 2021, and entitled “Method and Apparatus forCalibrating Well-Completion Techniques by Using Reinforcement Learning,”which is hereby incorporated herein by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

BACKGROUND

Embodiments disclosed herein generally relate to wellbore designs andwellbore completion operations. More particularly, embodiments disclosedherein relate to systems and methods for executing a frac packingoperation, including predicting a screen-out condition in an earthenformation through which a wellbore extends.

Wellbores are drilled into subterranean earthen formations to facilitatethe recovery of hydrocarbons from reservoirs within the earthenformation. In some applications, a drilled wellbore may be “completed”to enhance fluid conductivity or permeability between the wellbore andthe hydrocarbon bearing reservoir, and to improve extraction ofresources from the reservoir. In some completion operations, a hydraulicfracturing system is employed to initiate and propagate hydraulicfractures in the subterranean formation extending from the wellbore toenhance fluid conductivity between the wellbore and the surroundingformation. For instance, a hydraulic fracturing fluid may be pumped downthe wellbore against a desired location of the subterranean formation.The fluid is pressurized to a degree sufficient to initiate one or morefractures at the location along the formation.

Other completion techniques include gravel packing or “frac packing.”Frac packing is a completion technique that combines hydraulicfracturing and gravel packing. In a gravel packing or frac packingoperation, gravel is used as a filter that prevents unwanted formationmaterial from entering into the wellbore, while allowing hydrocarbons topass through. By using the frac pack technique, users/operators canachieve the advantages of improved production from hydraulic fracturing,while also achieving the advantage of sand control that is provided bygravel packing.

SUMMARY

In an example of the present disclosure, a system is provided for acompletion operation for a well extending through a subterranean earthenformation. The system includes a surface pump configured to pressurize afluid to a downhole net pressure measurable by a sensor package, a fluidline extending between the surface pump and a wellhead positioned at anupper end of the well, where the fluid line is configured to flow thefluid into the well, and a monitoring system in signal communicationwith the sensor package and comprising a reinforcement learning (RL)frac packing module stored in a memory of the monitoring system, whereinthe RL frac packing module is configured to a) perform an initialsimulation based on geological data of the well and an input parameter,where the initial simulation provides simulated net pressure values as afunction of time for the well; b) receive an indication of an actual netpressure value in the well; c) adjust the input parameter based on adifference between the actual net pressure value and a correspondingsimulated net pressure value; d) perform an updated simulation based onthe geological data of the well and the adjusted input parameter, wherethe updated simulation provides updated simulated net pressure values asa function of time for the well; e) iteratively adjust the inputparameter by repeating step c) and step d), with the correspondingsimulated net pressure value being from the updated simulated netpressure values; and f) provide an indication of an event at the wellbased on the actual net pressure value and the corresponding simulatednet pressure value. The event at the well a tip screen-out event in someexamples, and the indication is provided responsive to the differencebetween the actual net pressure value and the corresponding simulatednet pressure value being less than a first threshold amount, and a slopeof actual net pressure values deviating from a slope of simulated netpressure values by more than a second threshold amount.

In another example of the present disclosure, a method is provided for acompletion operation of a well. The method includes a) performing, by asimulator, an initial simulation based on geological data of the welland an input parameter, where the initial simulation provides simulatednet pressure values as a function of time for the well; b) receiving anindication of an actual net pressure value in the well; c) adjusting, bya reinforcement learning (RL) agent, the input parameter to thesimulator based on a difference between the actual net pressure valueand a corresponding simulated net pressure value; d) performing, by thesimulator, an updated simulation based on the geological data of thewell and the adjusted input parameter, where the updated simulationprovides updated simulated net pressure values as a function of time forthe well; e) iteratively adjusting the input parameter to the simulatorby repeating step c) and step d), with the corresponding simulated netpressure value being from the updated simulated net pressure values; andf) providing an indication of an event at the well based on the actualnet pressure value and the corresponding simulated net pressure value.The event at the well a tip screen-out event in some examples, and theindication is provided responsive to the difference between the actualnet pressure value and the corresponding simulated net pressure valuebeing less than a first threshold amount, and a slope of actual netpressure values deviating from a slope of simulated net pressure valuesby more than a second threshold amount.

In yet another example of the present disclosure, a non-transitorymachine-readable medium contains instructions that, when executed by aprocessor, cause the processor to a) perform an initial simulation basedon geological data of a well extending through a subterranean earthenformation and based on an input parameter, where the initial simulationprovides simulated net pressure values as a function of time for thewell; b) receive an indication of an actual net pressure value in thewell; c) adjust the input parameter based on a difference between theactual net pressure value and a corresponding simulated net pressurevalue; d) perform an updated simulation based on the geological data ofthe well and the adjusted input parameter, where the updated simulationprovides updated simulated net pressure values as a function of time forthe well; e) iteratively adjust the input parameter by repeating step c)and step d), with the corresponding simulated net pressure value beingfrom the updated simulated net pressure values; and f) provide anindication of an event at the well based on the actual net pressurevalue and the corresponding simulated net pressure value. The event atthe well a tip screen-out event in some examples, and the indication isprovided responsive to the difference between the actual net pressurevalue and the corresponding simulated net pressure value being less thana first threshold amount, and a slope of actual net pressure valuesdeviating from a slope of simulated net pressure values by more than asecond threshold amount.

Embodiments described herein comprise a combination of features andcharacteristics intended to address various shortcomings associated withcertain prior devices, systems, and methods. The foregoing has outlinedrather broadly the features and technical characteristics of thedisclosed embodiments in order that the detailed description thatfollows may be better understood. The various characteristics andfeatures described above, as well as others, will be readily apparent tothose skilled in the art upon reading the following detaileddescription, and by referring to the accompanying drawings. It should beappreciated that the conception and the specific embodiments disclosedmay be readily utilized as a basis for modifying or designing otherstructures for carrying out the same purposes as the disclosedembodiments. It should also be realized that such equivalentconstructions do not depart from the spirit and scope of the principlesdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of various exemplary embodiments, referencewill now be made to the accompanying drawings in which:

FIG. 1 is a schematic view of a system for enhancing fluid connectivitybetween a well and a subterranean earthen formation in accordance withthe principles disclosed herein;

FIG. 2 is a schematic view of an embodiment of a dendritic fracturesystem formed by the system of FIG. 1 and extending from the well ofFIG. 1 ;

FIG. 3 is a schematic view of an example of training a reinforcementlearning frac packing module in accordance with the principles disclosedherein;

FIG. 4 is a flowchart of an embodiment of a method for calibrating afrac pack simulator during a completion operation of a well extendingthrough a subterranean formation in accordance with the principlesdisclosed herein;

FIG. 5 is a graph of exemplary simulated net pressure values and actualnet pressure values provided by one or more embodiments in accordancewith the principles disclosed herein; and

FIG. 6 is a block diagram of an embodiment of a computer system inaccordance with the principles disclosed herein.

DETAILED DESCRIPTION

The following discussion is directed to various exemplary embodiments.However, one skilled in the art will understand that the examplesdisclosed herein have broad application, and that the discussion of anyembodiment is meant only to be exemplary of that embodiment, and notintended to suggest that the scope of the disclosure, including theclaims, is limited to that embodiment.

Certain terms are used throughout the following description and claimsto refer to particular features or components. As one skilled in the artwill appreciate, different persons may refer to the same feature orcomponent by different names. This document does not intend todistinguish between components or features that differ in name but notfunction. The drawing figures are not necessarily to scale. Certainfeatures and components herein may be shown exaggerated in scale or insomewhat schematic form and some details of conventional elements maynot be shown in interest of clarity and conciseness.

In the following discussion and in the claims, the terms “including” and“comprising” are used in an open-ended fashion, and thus should beinterpreted to mean “including, but not limited to . . . .” Also, theterm “couple” or “couples” is intended to mean either an indirect ordirect connection. Thus, if a first device couples to a second device,that connection may be through a direct connection of the two devices,or through an indirect connection that is established via other devices,components, nodes, and connections. In addition, as used herein, theterms “axial” and “axially” generally mean along or parallel to aparticular axis (e.g., central axis of a body or a port), while theterms “radial” and “radially” generally mean perpendicular to aparticular axis. For instance, an axial distance refers to a distancemeasured along or parallel to the axis, and a radial distance means adistance measured perpendicular to the axis. Any reference to up or downin the description and the claims is made for purposes of clarity, with“up”, “upper”, “upwardly”, “uphole”, or “upstream” meaning toward thesurface of the borehole and with “down”, “lower”, “downwardly”,“downhole”, or “downstream” meaning toward the terminal end of theborehole, regardless of the borehole orientation. As used herein, theterms “approximately,” “about,” “substantially,” and the like meanwithin 10% (i.e., plus or minus 10%) of the recited value. Thus, forexample, a recited angle of “about 80 degrees” refers to an angleranging from 72 degrees to 88 degrees.

The completion operations of this disclosure are generally describedwith reference to hydrocarbon wells. However, such completion operationsmay also be applied to geothermal energy extraction examples, as well ascarbon-capture-utilization-storage (CCUS) well examples. The scope ofthe present disclosure is not intended to be limited to a particulartype of well unless explicitly stated.

The present disclosure relates generally to calibrating well-completiontechniques and, more specifically, to calibrating fracturing-and-gravelpacking (or frac packing) models by using reinforcement learning.

During a frac packing process, a viscous fracturing fluid is pumped intothe well to create fractures. Subsequently, a mixture of fracturingfluid and proppant, such as sand, is pumped into the well to prevent thehydraulic fractures from closing following the conclusion of thehydraulic fracturing operation. During the frac packing process, apressure signal is referred to as net pressure, which is an additionalpressure over a fracture closure pressure that is useful to propagateand pack a fracture with proppant. As proppant enters the periphery ofthe fracture, the proppant bridges off and inhibits additional fracturegrowth. A tip screen-out (TSO) event refers to the point at which theproppant inhibits additional fracture growth, and such TSO event ischaracterized by a distinct change (e.g., an increase) in the netpressure slope as a function of time. Following the TSO event,additional proppant that enters the fracture may inflate the fractureand results in a net pressure gain. The design and implementation of afrac packing plan involves managing an amount of net pressure gain afterthe TSO event, through decisions such as adding or subtracting proppantat the wellbore surface, reducing a pump rate for packing inside thewell bore, and the like, which improves the likelihood of a relativelyhigh-quality frac packing job, or frac pack.

Currently, users attempt to achieve a high-quality frac pack (e.g.,attempt to achieve an appropriate net pressure gain after the TSO event)by manually performing the aforementioned management decisions in orderto arrive at the desired pressure signature. In order to manuallyperform these management decisions, the operators/users generally needto rely on their intuition and upon the output readings of a third-partysimulator. Specifically, currently, the timing of when and how to stopfilling in the proppant is performed manually by experiencedoperators/engineers. These experienced operators/engineers discuss andcome to an agreement on an action depending on personal priorexperience. The net pressure gain that is needed to maintain goodconnectivity between the earthen formation and the wellbore is dependenton many factors.

Accordingly, it is difficult to properly control a frac packing job.First, human operators relying on intuition can be inconsistent from onejob to the next. Second, frac packing jobs may occur over the course ofa 36-hour timeline, or longer in some cases, which subjects the humanoperators to fatigue that may further degrade their decision-makingabilities. Finally, human operators with expertise in thesewell-completion techniques are relatively scarce.

Embodiments disclosed herein address the foregoing by providing a fracpacking tool or module that enables operators to make improved decisionsat appropriate times, which facilitates efficient, consistent, andimproved frac packing operations. As described further below, the fracpacking module includes a reinforcement learning (RL) agent. The RLagent refers to computer-implemented functionality, such as a softwareprogram, and may be considered similar or equivalent to acomputer-implemented RL system, a computer-implemented RL engine, acomputer-implemented RL “brain”, and the like. The RL agent is not asaffected by human biases, and thus enables faster, more efficient fracpacking operations. The RL agent may also increase or maintain safetylevels, while reducing operator fatigue.

The frac packing module also includes a simulator that operates inconjunction with the RL agent. Accordingly, the combination of thesimulator and the RL agent is referred to herein as an RL frac packingmodule, for relative brevity. In some embodiments, the RL agent appliesa model-free, deep RL approach in order to improve and/or managecalibration of the simulator. In some embodiments, the calibration ofthe simulator occurs in real time, or in near-real time.

In some embodiments, an impending TSO event may be predicted oridentified using actual net pressure data (e.g., from a downhole gauge,or determined from a surface pressure gauge based on the hydrostaticpressure of the fluid column in the wellbore) and simulated net pressuredata from the simulator. For example, the simulator is configured todetermine (and display) simulations of net pressure as a function oftime for a given set of configurable input parameters, and for ageological profile of a given well (e.g., from logging data for thegiven well). The simulator is thus configured to provide simulated netpressure data that is predicted for the well. The RL agent is configuredto receive actual net pressure data, as well as the simulated netpressure data from the simulator. Based on the actual net pressure dataand the simulated net pressure data, the RL agent may iteratively adjustthe configurable input parameter(s) of the simulator (e.g., in realtime) in an attempt to improve the accuracy of the simulated netpressure data provided by the simulator. The simulator then updates thesimulated net pressure data based on the adjusted configurable inputparameter(s) from the RL agent, and thus may display updated simulationsof net pressure that more closely reflect (e.g., more closely match) theactual net pressure data for the well. The updated simulations can thenbe analyzed to determine whether a TSO event has occurred.

In some embodiments, the RL frac packing module is configured to providean alarm indicating the impending TSO event to an operator of a systemfor a well completion operation, or of a fracturing system performingpart of the well completion operation, allowing the operator to performone or more actions to achieve a desired net pressure gain after the TSOevent, which may result in an improved frac pack. For example, inresponse to receiving an alarm indicating a predicted TSO event (orotherwise identifying the TSO event), the operator may adjust one ormore properties of the fluid (e.g., fracturing fluid), such as a fluidflow rate, a surface pressure, introducing or reducing proppant, etc.,of the fluid to better achieve the desired net pressure gain followingthe TSO event. Although the description below specifically refers tofrac packing, one of ordinary skill will understand that certainembodiments of the present disclosure may be applied to various types ofsand- or proppant-laden pumping processes during completion operations.For example, the simulator tool can be used to simulate the evaluationsof net pressure traces including a tip screen-out, a magnitude of thenet pressure increase, and the like. These and other examples aredescribed more fully below, with reference made to the accompanyingfigures.

Referring now to FIGS. 1 and 2 , an embodiment of a hydraulic fracturingsystem 100 is shown. Fracturing system 100 is generally configured toenhance fluid communication or connectivity between a wellbore or well102 and a hydrocarbon bearing reservoir disposed in a subterraneanearthen formation 10 (shown in FIG. 2 ) through which well 102 extends.In other words, the earthen formation 10 is disposed about the well 102.As will be discussed in more detail below, fracturing system 100 is usedto initiate and propagate fractures in the earthen formation 10extending radially from well 102 in response to the communication ofpressurized hydraulic fracturing fluids into well 102. Such fractures inearthen formation 10 increase fluid connectivity between the well 102and the targeted subterranean reservoir within earthen formation 10.Thus, fracturing system 100 is employed to perform hydraulic fracturingoperations, including frac packing operations and in particular,fracturing operations designed to increase the complexity of fracturesformed in the earthen formation 10 to enhance fluid connectivity betweenthe well 102 and the targeted reservoir within earthen formation 10.

As best shown in FIG. 1 , fracturing system 100 may generally include afracturing fluid supply conduit or manifold 104, a plurality of fluidstorage tanks 106, a hydration unit 108, a blending unit 110, afracturing manifold 114, a plurality of high pressure surface pumps 116,a high pressure fracturing line 118, a frac tree 120 (including a flowcross), a wellhead 122, a flowback line 124, and a flowback tank 126.Storage tanks 106 and units 108 and 110 are in series fluidcommunication with conduit 104. In the embodiment shown in FIG. 1 ,fluid storage tanks 106 store a base fluid (e.g., water) that is routedthrough the hydration unit 108 where chemicals, such as e.g., gellingagents, are added. After sufficient hydration time to allow for adequatemixing in hydration unit 108, the mixed fluid may travel to blendingunit 110 and be blended with predetermined quantities of chemicals(e.g., acids, surfactants, gels, emulsifiers, viscosity reducers,friction reducers, etc.) and/or diverting agents provided by blendingunit 110. In this embodiment, blending unit 110 supplies hydraulicfracturing system 100 with a permanent ornon-dissolvable/non-biodegradable diverting agent, such as proppant(e.g., sand, etc.). Examples of suitable permanent diverting agentsinclude, without limitation, 100 mesh proppant, walnut hulls, largegrain size proppants (e.g., 8-16 to 40-70 mesh proppants), or any otherparticulate which remains in the reservoir. The permanent divertingagent(s) provided by blending unit 110 to the mixed fluid are designedto assist in preventing fracturing fluid from leaking into the earthenformation 10 from a fracture formed therein, and, as will be discussedfurther herein.

Referring still to FIG. 1 , the blended hydraulic fracturing fluidprovided by blending unit 110 is supplied to fracturing manifold 114,where the fracturing fluid may be routed through the plurality ofsurface pumps 116 to pressurize the fracturing fluid to a fracturingpressure sufficient to initiate or form one or more fractures in theearthen formation 10 through which the well 102 extends. In thisembodiment, each fracturing pump 116 comprises a positive displacementpump powered by a power source such as a diesel engine, a gas turbine,or other devices known in the art; however, in other embodiments, theconfiguration of each fracturing pump 116 may vary. Accordingly, in atleast this embodiment, shutting down or de-energizing the power sourceof each pump 116 holds each pump 116 stationary such that back pressuremay be held within the well 102.

Following pressurization via surface pumps 116, the fracturing fluid maybe routed from manifold 114 through high pressure fracturing line 118into frac tree 120. Thus, high pressure fracturing line 118 extends andprovides fluid communication between manifold 114 and frac tree 120.Frac tree 120 manages the flow and communication of fluid between well102 and the components (e.g., high pressure fracturing line 118,flowback line 124, etc.) of fracturing system 100. In this embodiment,frac tree 120 comprises a flow cross and is coupled with high pressurefracturing line 118, flowback line 124, and wellhead 122; however, inother embodiments, the configuration of frac tree 120 may vary. Fluidcommunication between frac tree 120 and well 102 is provided by wellhead122 disposed at the upper end of well 102 (at the surface). Wellhead 122provides physical support for frac tree 120 as well as components offracturing system 100 that extend into well 102, including, for example,a casing string (not shown in FIG. 1 ). The casing string providesstructural support to well 102 and controls fluid communication betweenwell 102 and the surrounding earthen formation 10 through which well 102extends. Although well 102 is a cased well in this embodiment, ingeneral, system 100 can be used in connection with cased or uncasedwells.

In the arrangement described above and shown in FIG. 1 , pressurizedfracturing fluid is communicated from high pressure fracturing line 118,through frac tree 120, and into well 102 via wellhead 122. As will bediscussed further herein, hydraulic fracturing system 100 also routesflowback fluid received from well 102 following the injection ofpressurized fracturing fluid into well 102. Particularly, flowback fluidfrom well 102 may be routed through wellhead 122, frac tree 120, andinto flowback tank 126 via flowback line 124 for storage in tank 126. Inthis embodiment, frac tree 120 includes a frac valve 128 for isolatinghigh pressure fracturing line 118 when flowback fluid is flowed fromwell 102 into flowback tank 126 via flowback line 124. Additionally,frac tree 120 includes a flowback valve 130 for isolating flowback tank126 when high pressure fracturing fluid is pumped into well 102 fromhigh pressure fracturing line 118; however, in other embodiments, theconfiguration of frac tree 120 may vary.

Referring still to FIG. 1 , high pressure fracturing line 118 includes asurface sensor package or assembly 132 to measure and indicate the fluidpressure of the fracturing fluid within high pressure fracturing line118. In this embodiment, fluid injection or flow rate of pressurizedfracturing fluid through high pressure fracturing line 118 may bedetermined by parameters pertaining to surface pumps 116 (i.e., speed,amount of fluid displaced each period, etc.), via surface sensor package132. In general, the embodiments described herein are not limited toobtaining pressure data from a particular location, and should beinterpreted to encompass examples in which net pressure is determinedfrom one or more sensors downhole, one or more sensors at the surface,or combinations thereof.

In this embodiment, fracturing system 100 further includes a monitoringsystem 134 for monitoring various parameters of well 102 and equipmentof fracturing system 100. Monitoring system 134 is in signalcommunication with surface sensor package 132 (and/or one or moredownhole sensors, not shown in FIG. 1 for simplicity) and otherequipment of fracturing system 100. Monitoring system 134 is also incommunication with an input/output (I/O) unit 136 (e.g., a graphicaluser interface, a touchscreen interface, or the like) for displayinginformation to an operator of fracturing system 100 and for receivinguser inputs from the operator. The I/O unit 136 may display informationrelated to the operation of fracturing system 100 and may receive userinputs related to operation of fracturing system 100. During operations,I/O unit 136 may communicate received user inputs to monitoring system134, including the viscosity of the fracturing fluid supplied to well102, and mesh size and density of the diverting agent provided byblending unit 110. Communication between the I/O unit 136 and monitoringsystem 134 may be wired, wireless, or a combination thereof.

Monitoring system 134 comprises any suitable device or assembly which iscapable of receiving electrical (or other data) signals and transmittingelectrical (or other data) signals to other devices. In particular,while not specifically shown, monitoring system 134 may comprise acomputer system including a processor and a memory. The processor (e.g.,microprocessor, central processing unit, or collection of such processordevices, etc.) may execute machine readable instructions (e.g.,non-transitory machine readable medium) provided on the correspondingmemory to provide the processor with all of the functionality describedherein. The memory of monitoring system 134 may comprise volatilestorage (e.g., random access memory), non-volatile storage (e.g., flashstorage, read only memory, etc.), or combinations of both volatile andnon-volatile storage. Data consumed or produced by the machine readableinstructions can also be stored on the memory of monitoring system 134.An RL frac packing module is stored in the memory of monitoring system134 and is executed by the processor of monitoring system 134. As willbe described further herein, the RL frac packing module is generallyconfigured to provide an alarm or another indication of an impending TSOevent in an earthen formation (e.g., earthen formation 10), during theperformance of a hydraulic fracturing operation. This enables theoperator to perform one or more actions to achieve a desired netpressure gain after the TSO event, which may result in an improved fracpack. Although in this embodiment monitoring system 134 comprises acomponent of the fracturing system 100 shown in FIG. 1 , in otherembodiments, monitoring system 134 may be utilized in fracturing systemswhich vary in configuration from fracturing system 100.

Hydraulic fracturing system 100 may be employed via methods discussedfurther herein to form a fracture system within earthen formation 10, asshown schematically in FIG. 2 . Particularly, FIG. 2 schematicallyillustrates a horizontally extending (relative to the surface),subsurface portion of well 102 and the surrounding earthen formation 10through which well 102 extends. Although in this embodiment well 102 ofhydraulic fracturing system 100 includes a horizontal or deviatedsection, in other embodiments, well 102 may comprise a substantiallyvertical well.

As shown particularly in FIG. 2 , by injecting fracturing fluid intowell 102 at a sufficient pressure, fracturing system 100 may form a pairof primary fractures 152 within earthen formation 10. In thisembodiment, fractures 152 extend from a generally cylindrical surface153 of the well 102 in a bi-wing configuration. Fractures 152 generallycomprise initial hydraulic (i.e., not natural) fractures formed inearthen formation 10 during a hydraulic fracturing operation (e.g., afrac packing operation) performed by the hydraulic fracturing system 100shown in FIG. 1 .

Referring now to FIG. 3 , a schematic example of training a RL fracpacking module 305 is shown. As described above, certain embodimentsimprove and/or the well-completion process using reinforcement learning.Certain embodiments of the present invention integrate a reinforcementlearning system (e.g., RL agent) with a third-party simulator for fracpacks.

Reinforcement learning seeks to train an agent, which “learns” how toprovide optimization guidance or make independent decisions based oninteractions with a simulator. The agent “learning” is based onreceiving either a reward or a penalty based on its previous recommendedaction (e.g., adjusting an input parameter for the simulator) and theresulting simulator output that is provided to the agent.

In the example of FIG. 3 , the RL frac packing module 305 is representedby a simulator 310 (i.e., the simulator of an RL methodology) and an RLagent 320 (i.e., the agent of the RL methodology). The simulator 310 andthe RL agent 320 may be connected by an application programminginterface (API), such as the Python API. In some examples, final resultsof calibration of the simulator 310 (e.g., when simulator 310 inputparameter(s) are provided by the RL agent 320 such that simulated netpressure values are within a threshold of actual net pressure values)may be provided to a display coupled to the RL frac packing module 305.

Also, in FIG. 3 , actions 330 represents adjustments to simulator 310input parameter(s) that are implemented by the RL agent 320, such as toiteratively configure the simulator 310 based on actual net pressurevalues. In FIG. 3 , state block 340 represents a comparison of simulatednet pressure values (P_(net_pred)) from the simulator 310 to actual netpressure values (P_(net_field)), represented by or received as fielddata 350. As described above, the field data 350 may be from a downholegauge, or determined from a surface pressure gauge based on thehydrostatic pressure of the fluid column in the wellbore 102. In thisexample, the state block 340 determines the difference between actualnet pressure value(s) (from the field data 350) and simulated netpressure value(s) (from the simulator 310) as a mean squared error (MSE)calculation, and provides the result of the MSE calculation (e.g.,MSE(P_(net_pred)−P_(net_field))) back to the RL agent 320.

Generally, FIG. 3 demonstrates a methodology that includes training areinforcement learning system (e.g., the RL agent 320) to optimize orimprove the calibration of the simulator 310 (e.g., while the simulator310 is providing simulated net pressure values for a frac packoperation), in real time (or near-real time) during the frac packoperation.

The RL agent 320 may be trained to configure the simulator 310 such thatthe simulated net pressure values generated by the simulator 310 match,or are within a threshold amount of, the measured net pressure valuesfrom the well 102, such as from field data 350. As described above,measured net pressure values may be determined from field data 350, suchas that received from a sensor package, which may include a downholepressure gauge, a surface pressure sensor, or combinations thereof. Thefield data 350 may also include bottom hole proppant concentration data,surface tubing and annulus pressure data, data related to a volume ofproppant remaining at the surface, washpipe pressure data, orcombinations thereof.

In some embodiments, the RL agent 320 iteratively configures thesimulator 310 (e.g., in real time or near-real time), such as bymodifying or otherwise adjusting simulator 310 input parameter(s)related to the well 102 (represented by actions 330). For example, thesimulator 310 input parameter(s) may include a modulus configurationparameter, a toughness configuration parameter, a stress configurationparameter, and/or a leakoff coefficient configuration parameter for thewell 102. As described above, the simulator 310 may be considered to beproperly configured when the simulated net pressure values match, or arewithin a threshold amount of, the actual (e.g., measured) net pressurevalues. Accordingly, the RL agent 320 configures the simulator 310 inreal time, or in near-real time.

The RL agent 320 may be configured to be trained using a rewardfunction. For example, the reward function may be such that the RL agent320 is rewarded for configurations (of the simulator 310 inputparameter(s)) that result in simulated net pressure values that arecloser to the actual, measured net pressure values 350 from the well102.

In an embodiment, the simulator 310 is configured to perform an initialsimulation, which may be based on geological data of the well 102 (e.g.,from one or more logging operations performed for the well 102) and oneor more input parameters. As described above, the input parameter(s) mayinclude a modulus, a toughness, a stress, and/or a leakoff coefficientof the well 102. The initial simulation performed by the simulator 310provides simulated net pressure values (P_(net_pred)), which may beprovided as a function of time for the well 102.

The state block 340 receives the simulated net pressure values from thesimulator 310, and also receives the actual net pressure values of thewell 102 (P_(net_field)) from the field data 350. The state block 340compares the simulated net pressure values with the actual net pressurevalues, and provides an indication of a difference between the simulatednet pressure values and the actual net pressure values. For example, theindication of the difference is a result of an MSE calculation. The MSEcalculation is then provided as an input to the RL agent 320.

The RL agent 320 is configured to adjust the input parameter(s) to thesimulator 310, as actions 330 to the simulator 310, based on theindication of difference received from the state block 340. For example,the RL agent 320 adjusts the input parameter(s) to the simulator 310such that the simulator 310 provides simulated net pressure values thatare closer to the actual net pressure values received as field data 350.

The simulator 310 is configured to perform an updated simulation basedon the already-determined geological data of the well 102 as well as theadjusted input parameter(s) provided as actions 330 by the RL agent 320.The updated simulation provides updated simulated net pressure values(P_(net_pred)) as a function of time for the well 102.

The above-described process continues iteratively (e.g., in a loopfashion as shown in FIG. 3 ) until the simulated net pressure valuesfrom the simulator 310 are within a threshold amount of the actual netpressure values indicated by the field data 350. For example, with eachiteration, the RL agent 320 configures the simulator 310 by adjustinginput parameter(s) using certain actions 330. Subsequently, thesimulator 310 generates an updated simulated net pressure(P_(net,pred)), and the state block 340 performs an MSE calculationbased on the updated simulated net pressure values and the actual netpressure values indicated by the field data 350, the result of which MSEcalculation is provided back into the RL agent 320, for further trainingof the RL agent 320 to more accurately configure the simulator 310.

In some embodiments, the state block 340 particularly provides anindication of the difference (e.g., the result of an MSE calculation)between an actual net pressure value, indicated by the field data 350,and a corresponding simulated net pressure value from the simulator 310.A corresponding simulated net pressure value may refer to a simulatednet pressure value that is simulated for (e.g., predicted) a particulartime that is approximately the same as the time associated with theactual net pressure value. For example, field data 350 provides actualnet pressure values beginning at time 0, and every 5 seconds thereafter.In this example, corresponding simulated net pressure values may be thenet pressure values provided by the simulator 310 for the same time 0,and every 5 seconds thereafter. In this way, the state block 340compares actual net pressure values with net pressure values that aresimulated for approximately the same time, which enables the RL agent320 to be further trained based on differences between the actual netpressure values from the field data 350 and corresponding simulated netpressure values from the simulator 310.

After the RL agent 320 iteratively configures the simulator 310 toprovide simulated net pressure values that match (or are within athreshold of) the actual readings of net pressure, the RL frac packingmodule 305 may be configured to provide an indication of a TSO event.For example, a TSO event may be indicated when the simulated netpressure values are sufficiently close to the actual net pressure values(e.g., the difference between the simulated and actual net pressurevalues is less than a first threshold amount), and a slope (e.g., a rateof change) of the actual net pressure values deviates from a slope ofthe simulated net pressure values by more than a second thresholdamount. As described above, the TSO event refers to the point at whichthe proppant inhibits additional fracture growth, and such TSO event ischaracterized by a distinct change (e.g., an increase) in the netpressure slope as a function of time. In the example of FIG. 3 , suchdistinct change in slope of the actual net pressure values is determinedby its deviation from (e.g., by more than the second threshold amount)the simulated net pressure values from the simulator 310.

Following the TSO event, additional proppant that enters the fracturemay inflate the fracture and results in a net pressure gain. In someexamples, a frac packing plan is designed and implemented to manage anamount of net pressure gain after the TSO event, through decisions suchas adding or subtracting proppant at the wellbore surface, reducing apump rate for packing inside the well bore, and the like, which improvesthe likelihood of a relatively high-quality frac packing job, or fracpack.

In some examples, the RL frac packing module 305 is thus furtherconfigured to provide a recommendation to an operator to adjust the pumprate, surface pressure, and/or proppant volume to achieve a particularor desired net pressure gain following the TSO event. For example, theRL frac packing module 305 may display a simulation of net pressure(e.g., from the simulator 310), which enables the operator to determinewhat steps may be taken in order to achieve the desired amount of netpressure gain after the TSO event.

The recommendation from the RL frac packing module 305 may pertain toparameters useful to control a well-completion technique, such as a fracpacking operation. By adjusting proppant volume, changing the pump rate,and/or adjusting a choke openness, an operator may achieve a desired netpressure gain after the TSO event identified by the RL frac packingmodule 305.

In other examples, the RL frac packing module 305 is further configuredto automatically adjust the pump rate, surface pressure, and/or proppantvolume to achieve the particular or desired net pressure gain followingthe TSO event. For example, the RL frac packing module 305 may providecommand(s) to various control interface(s) in order to control varioussystems in order to adjust the desired parameters to achieve theparticular or desired net pressure gain following the TSO event.

The machine/reinforcement learning approach of the embodiments describedherein can thus be used to improve real-time decision making by anoperator, and/or to automate certain control actions in order to achievea particular or desired net pressure gain following the TSO event. Inother embodiments, a human operator may refer to generated simulations(e.g., from the simulator 310) to make an operator decision to achievethe desired net pressure gain. The RL agent 320 may be coupled to acustom user interface to enable or assist operational decisions by afrac pack engineer.

In some embodiments, the RL agent 320 may be trained prior to beingdeployed at, or used in conjunction with, a first well. For example, theRL agent 320 may be trained for a particular geographic region, and thensubsequently deployed on other wells in that geographic region. In onexample, a second well is in the same geographic region as the firstwell, and the RL agent 320 is trained using data collected from apreviously performed well completion operation for the second well.Subsequently, the RL agent 320 may be more quickly trained for the firstwell, due to its having been previously trained on the second well inthe same geographic region. The RL agent 320 may also be deployed onother wells in the same region.

In some embodiments, the RL agent 320 is configured to achieve animproved, more accurate net pressure gain (e.g., in terms of pounds/ft²of proppant formation) and a void-free annular pack in order to ensuregood connectivity to the reservoir. The RL agent 320 may also thusreduce issues or problems related to pumping operations during the fracpack.

In some embodiments, the RL agent 320 is trained in a simulationenvironment, such as prior to being deployed at, or used in conjunctionwith, a first well. For example, the simulation environment may act asthe learning environment for the RL agent 320. Using model-free RL, theRL agent 320 may be configured to learn (e.g., be trained) in asimulated environment, in which the RL agent 320 is observes (e.g.,receives data related to) the environment after every action (e.g.,adjusting input parameter(s) as in block 330 in FIG. 3 ) and furthercontinues to adjust the various input parameter(s) based on receiving a“reward” (e.g., which reward may be based on feedback such as that fromstate block 340). In some embodiments, the simulator 310 may implement aStimPlan™ simulation model, such the RL agent 320 may be configured tooptimizes or improve calibration coefficients of the simulation modelimplemented by the simulator 310. For example, one or more embodimentsof the RL agent 320 may be configured to optimize or improve calibrationcoefficients in a StimPlan™ simulation.

In some embodiments, the trained model implemented by the RL agent 320may be tested and/or otherwise validated in a simulation environment,and may also be benchmarked against historical frac packing operationsdata (e.g., data from previously-completed frac packs).

Turning now to FIG. 4 , a method 400 for calibrating a frac packsimulator during a completion operation is shown in further detail. Inparticular, the method 400 may be useful to calibrate the RL fracpacking module 305 described above, including training the RL agent 320thereof. Accordingly, the method 400 is useful for well 102 extendingthrough a subterranean formation.

The method 400 begins in block 402 with performing an initial simulationbased on geological data of the well 102 and an input parameter. Theinitial simulation provides simulated net pressure values as a functionof time for the well 102. As described above, the simulator 310 isconfigured to perform an initial simulation, which may be based ongeological data of the well 102 (e.g., from one or more loggingoperations performed for the well 102) and one or more input parameters.The input parameter(s) may include a modulus, a toughness, a stress,and/or a leakoff coefficient of the well 102. The initial simulationperformed by the simulator 310 provides simulated net pressure values(P_(net_pred)), which may be provided as a function of time for the well102.

The method 400 continues in block 404 with receiving an indication of anactual net pressure value in the well 102. As described above, fielddata 350 may be indicative of the actual net pressure values, and the RLfrac packing module 305 (e.g., the state block 340) is configured toreceive the field data 350 and to determine an actual net pressure valueassociated therewith.

The method 400 then continues in block 406 with adjusting the inputparameter to the simulator 310 based on a difference between the actualnet pressure value and a corresponding simulated net pressure value(e.g., as determined by the state block 340). The difference may includethe result of an MSE calculation between the actual net pressure valueand the corresponding simulated net pressure value. As described above,actions 330 represent adjustments to simulator 310 input parameter(s)that are implemented by the RL agent 320, such as to iterativelyconfigure the simulator 310 based on actual net pressure values. Thus,based on a comparison of simulated net pressure values (P_(net_pred))from the simulator 310 to actual net pressure values (P_(net_field)),represented by or received as field data 350, the RL agent 320 isconfigured to adjust the input parameter(s) to the simulator 310, asactions 330 to the simulator 310. For example, the RL agent 320 adjuststhe input parameter(s) to the simulator 310 such that the simulator 310provides simulated net pressure values that are closer to the actual netpressure values received as field data 350.

The method 400 continues further in block 408 with performing an updatedsimulation based on the geological data of the well 102 and the adjustedinput parameter. The updated simulation provides updated simulated netpressure values as a function of time for the well 102. As describedabove, the simulator 310 is configured to perform an updated simulationbased on the already-determined geological data of the well 102 as wellas the adjusted input parameter(s) provided as actions 330 by the RLagent 320. The updated simulation provides updated simulated netpressure values (P_(net_pred)) as a function of time for the well 102.

In block 410, the method 400 iteratively adjusts the input parameter tothe simulator 310 by repeating step 406 and step 408, with thecorresponding simulated net pressure value being from the updatedsimulated net pressure values. As described above with respect to FIG. 3, the process continues in a loop fashion until the simulated netpressure values from the simulator 310 are within a threshold amount ofthe actual net pressure values indicated by the field data 350. Forexample, with each iteration, the RL agent 320 configures the simulator310 by adjusting input parameter(s) using certain actions 330.Subsequently, the simulator 310 generates an updated simulated netpressure (P_(net,pred)), and the state block 340 performs an MSEcalculation based on the updated simulated net pressure values and theactual net pressure values indicated by the field data 350, the resultof which MSE calculation is provided back into the RL agent 320, forfurther training of the RL agent 320 to more accurately configure thesimulator 310.

Finally, the method 400 continues in block 412 with providing anindication of an event at the well based on the actual net pressurevalue and the corresponding simulated net pressure value. One example ofsuch a well event is a TSO event, as described above. In this example,the indication of the TSO event is provided responsive to the differencebetween the actual net pressure value and the corresponding simulatednet pressure value being less than a first threshold amount, and a slopeof actual net pressure values deviating from a slope of simulated netpressure values by more than a second threshold amount. For example, aTSO event may be indicated when the simulated net pressure values aresufficiently close to the actual net pressure values (e.g., thedifference between the simulated and actual net pressure values is lessthan a first threshold amount), and a slope (e.g., a rate of change) ofthe actual net pressure values deviates from a slope of the simulatednet pressure values by more than a second threshold amount.

FIG. 5 is a graph 500 of exemplary simulated net pressure values andactual net pressure values provided by one or more embodiments describedherein. In the graph 500, simulated net pressure values as a function oftime (e.g., from the simulator 310) are represented by 510, while actualnet pressure values as a function of time (e.g., from a pressure sensor)are represented by 520. As described above, the RL agent 320 implementsa reinforcement learning approach to iteratively configure the simulator310, such that, over time, the simulated net pressure values 510 moreclosely match the actual net pressure values 520.

As the RL agent 320 iteratively configures the simulator 310 based onthe actual net pressure values 520, the simulated net pressure 510begins to converge with the actual net pressure values 520. Presentingthe graph 500 to an operator (e.g., which graph 500 may be updated overtime according to the iterative reinforcement learning process describedabove) may allow the operator to ascertain whether a TSO event hasoccurred. In the example of FIG. 5 , a TSO event occurs at time 530(e.g., at approximately 20 minutes elapsed in the graph 500). Asdescribed above, the TSO event is characterized by a distinct change(e.g., increase) in slope of the actual net pressure 520, particularlyrelative to the simulated net pressure 510.

In this example, the indication of the TSO event is provided responsiveto the difference between the actual net pressure value 520 and thecorresponding simulated net pressure value 510 being less than a firstthreshold amount, and a slope of actual net pressure values 520deviating from a slope of simulated net pressure values 510 by more thana second threshold amount. At time 530, such deviation occurs becausethe simulated net pressure values 510 were sufficiently close to theactual net pressure values 520 (e.g., indicating an approximatelyaccurate calibration of input parameter(s) of the simulator 310), andthe slope of the actual net pressure values 520 increased relative tothe simulated net pressure values 510. As described above, the“closeness” or accuracy of the simulated net pressure values 510relative to the actual net pressure values 520 may be determinedrelative to a first threshold value. Also, the deviation of the slope ofthe actual net pressure values 520 relative to the simulated netpressure values 510 may be relative to a second threshold value.

At the time 530 of the TSO event, an operator may introduce or reduce anamount of proppant to affect the net pressure gain following the time530 of the TSO event. In the example of FIG. 5 , the desired gain in netpressure (e.g., following the TSO event at 530) is approximately 500 psi(i.e., the difference between about 1200 psi and about 700 psi). Otherexamples may have different amounts of desired gain in net pressure. Byachieving the desired net pressure gain after the TSO event at 530, theoperators may achieve a more high-quality frac pack.

Following the TSO event at 530, an increasing amount of proppant isillustrated by 540. As described above, during the frac packing process,fluid is pumped first followed by proppant. The increasing amount ofproppant 540 can be pumped after the TSO event at 530. As the amount ofproppant 540 increases, the actual net pressure 520 builds as a functionof time.

In some scenarios, as the actual net pressure 520 increases, theincreased pressure can also lead to a “well bore screen out,” which mayindicate that the frac packing is complete. Keeping various volume andpressure states into consideration, and viewing the graph 500 (and/orresponding to recommendations or automated actions as described above)the operator is able to manage ceasing to provide (e.g., fill with)proppant at an appropriate time prior to a time at which the well borescreen out would otherwise occur.

The foregoing embodiments may reduce pumping problems—such as byimproving safety, reducing cost, and reducing problems associated withdeferred production—due to unmanaged screen outs during frackingoperations, including frac packing operations. Accordingly, theforegoing embodiments may help achieve a higher near-wellbore proppantconcentration, which enhances fluid connectivity to the reservoir.

For example, certain embodiments disclosed herein may increase a successrate for creating high-conductivity fractures in an ultra-highpermeability environment. Certain embodiments may also increase asuccess rate for proppant placement, where high fluid leak-off rates areevident.

In other low-carbon scenarios, the disclosed embodiments may apply wherethere is a need to drill wells into reservoirs, and may increase volumeof CO2 stored in such reservoirs. In a geothermal energy productionscenario, the disclosed embodiments may enable drilling of relativelydeep wells, while increasing or maximizing the surface area for heattransfer.

Referring to FIG. 6 , an embodiment of a computer system 600 suitablefor implementing one or more embodiments disclosed herein is shown. Forexample, the monitoring system 134 shown in FIG. 1 may be configured ina manner similar to the computer system 600 shown in FIG. 6 . Thecomputer system 600 includes a processor 602 (which may be referred toas a central processor unit or CPU) that is in communication with one ormore memory devices 604, and input/output (I/O) devices 606. Theprocessor 602 may be implemented as one or more CPU chips. The memorydevices 604 of computer system 600 may include secondary storage (e.g.,one or more disk drives, etc.), a non-volatile memory device such asread only memory (ROM), and a volatile memory device such as randomaccess memory (RAM). In some contexts, the secondary storage ROM, and/orRAM comprising the memory devices 604 of computer system 600 may bereferred to as a non-transitory computer readable medium or a computerreadable storage media. I/O devices 326 may include printers, videomonitors, liquid crystal displays (LCDs), touch screen displays,keyboards, keypads, switches, dials, mice, and/or other well-known inputdevices.

It is understood that by programming and/or loading executableinstructions onto the computer system 600, at least one of the CPU 602,the memory devices 604 are changed, transforming the computer system 600in part into a particular machine or apparatus having the novelfunctionality taught by the present disclosure. Additionally, after thecomputer system 600 is turned on or booted, the CPU 602 may execute acomputer program or application. For example, the CPU 602 may executesoftware or firmware stored in the memory devices 604. The softwarestored in the memory devices 604 and executed by CPU 602 may comprisethe RL frac packing module 305 shown in FIG. 3 . During execution, anapplication may load instructions into the CPU 602, for example loadsome of the instructions of the application into a cache of the CPU 602.In some contexts, an application that is executed may be said toconfigure the CPU 602 to do something, e.g., to configure the CPU 602 toperform the function or functions promoted by the subject application.When the CPU 602 is configured in this way by the application, the CPU602 becomes a specific purpose computer or a specific purpose machine.

While exemplary embodiments have been shown and described, modificationsthereof can be made by one skilled in the art without departing from thescope or teachings herein. The embodiments described herein areexemplary only and are not limiting. Many variations and modificationsof the systems, apparatus, and processes described herein are possibleand are within the scope of the disclosure. For example, the relativedimensions of various parts, the materials from which the various partsare made, and other parameters can be varied. Accordingly, the scope ofprotection is not limited to the embodiments described herein, but isonly limited by the claims that follow, the scope of which shall includeall equivalents of the subject matter of the claims. Unless expresslystated otherwise, the steps in a method claim may be performed in anyorder. The recitation of identifiers such as (a), (b), (c) or (1), (2),(3) before steps in a method claim are not intended to and do notspecify a particular order to the steps, but rather are used to simplifysubsequent reference to such steps.

What is claimed is:
 1. A method for a completion operation of a well,the method comprising: a) performing, by a simulator, an initialsimulation based on geological data of the well and an input parameter,wherein the initial simulation provides simulated net pressure values asa function of time for the well; b) receiving an indication of an actualnet pressure value in the well; c) adjusting, by a reinforcementlearning (RL) agent, the input parameter to the simulator based on adifference between the actual net pressure value and a correspondingsimulated net pressure value; d) performing, by the simulator, anupdated simulation based on the geological data of the well and theadjusted input parameter, wherein the updated simulation providesupdated simulated net pressure values as a function of time for thewell; e) iteratively adjusting the input parameter to the simulator byrepeating step c) and step d), with the corresponding simulated netpressure value being from the updated simulated net pressure values; andf) providing an indication of an event at the well based on the actualnet pressure value and the corresponding simulated net pressure value.2. The method of claim 1, wherein the event at the well comprises a tipscreen-out event, and wherein the indication is provided responsive tothe difference between the actual net pressure value and thecorresponding simulated net pressure value being less than a firstthreshold amount, and a slope of actual net pressure values deviatingfrom a slope of simulated net pressure values by more than a secondthreshold amount.
 3. The method of claim 1, further comprising providinga recommendation to an operator to adjust a pump rate, a surfacepressure, or a proppant volume to achieve a particular net pressure gainfollowing the event.
 4. The method of claim 1, further comprisingautomatically adjusting a pump rate, a surface pressure, a proppantvolume, or combinations thereof responsive to the event.
 5. The methodof claim 1, further comprising, before step a), performing a welllogging operation to generate the geological data for the initialsimulation.
 6. The method of claim 1, wherein a time associated with thecorresponding simulated net pressure value is approximately the same asa time associated with the actual net pressure value.
 7. The method ofclaim 1, wherein the input parameter comprises a modulus of the well, atoughness of the well, a stress of the well, a leakoff coefficient ofthe well, or combinations thereof.
 8. The method of claim 1, wherein thedifference between the actual net pressure value and the correspondingsimulated net pressure value comprises a mean squared error calculation.9. A system for a completion operation for a well extending through asubterranean earthen formation, the system comprising: a surface pumpconfigured to pressurize a fluid to a downhole net pressure measurableby a sensor package; a fluid line extending between the surface pump anda wellhead positioned at an upper end of the well, wherein the fluidline is configured to flow the fluid into the well; and a monitoringsystem in signal communication with the sensor package and comprising areinforcement learning (RL) frac packing module stored in a memory ofthe monitoring system, wherein the RL frac packing module is configuredto: a) perform an initial simulation based on geological data of thewell and an input parameter, wherein the initial simulation providessimulated net pressure values as a function of time for the well; b)receive an indication of an actual net pressure value in the well; c)adjust the input parameter based on a difference between the actual netpressure value and a corresponding simulated net pressure value; d)perform an updated simulation based on the geological data of the welland the adjusted input parameter, wherein the updated simulationprovides updated simulated net pressure values as a function of time forthe well; e) iteratively adjust the input parameter by repeating step c)and step d), with the corresponding simulated net pressure value beingfrom the updated simulated net pressure values; and f) provide anindication of an event at the well based on the actual net pressurevalue and the corresponding simulated net pressure value.
 10. The systemof claim 9, wherein the event at the well comprises a tip screen-outevent, and wherein the indication is provided responsive to thedifference between the actual net pressure value and the correspondingsimulated net pressure value being less than a first threshold amount,and a slope of actual net pressure values deviating from a slope ofsimulated net pressure values by more than a second threshold amount.11. The system of claim 9, wherein the RL frac packing module is furtherconfigured to provide a recommendation to an operator to adjust a pumprate, a surface pressure, or a proppant volume to achieve a particularnet pressure gain following the event.
 12. The system of claim 9,wherein the RL frac packing module is further configured toautomatically adjust a pump rate of the surface pump, a surfacepressure, a proppant volume, or combinations thereof responsive to theevent.
 13. The system of claim 9, wherein a time associated with thecorresponding simulated net pressure value is approximately the same asa time associated with the actual net pressure value.
 14. The system ofclaim 9, wherein the input parameter comprises a modulus of the well, atoughness of the well, a stress of the well, a leakoff coefficient ofthe well, or combinations thereof.
 15. The system of claim 9, whereinthe difference between the actual net pressure value and thecorresponding simulated net pressure value comprises a mean squarederror calculation.
 16. A non-transitory computer-readable mediumincluding instructions that, when executed by a processor, cause theprocessor to: a) perform an initial simulation based on geological dataof a well extending through a subterranean earthen formation and basedon an input parameter, wherein the initial simulation provides simulatednet pressure values as a function of time for the well; b) receive anindication of an actual net pressure value in the well; c) adjust theinput parameter based on a difference between the actual net pressurevalue and a corresponding simulated net pressure value; d) perform anupdated simulation based on the geological data of the well and theadjusted input parameter, wherein the updated simulation providesupdated simulated net pressure values as a function of time for thewell; e) iteratively adjust the input parameter by repeating step c) andstep d), with the corresponding simulated net pressure value being fromthe updated simulated net pressure values; and f) provide an indicationof an event at the well based on the actual net pressure value and thecorresponding simulated net pressure value.
 17. The non-transitorycomputer-readable medium of claim 16, wherein the event at the wellcomprises a tip screen-out event, and wherein the indication is providedresponsive to the difference between the actual net pressure value andthe corresponding simulated net pressure value being less than a firstthreshold amount, and a slope of actual net pressure values deviatingfrom a slope of simulated net pressure values by more than a secondthreshold amount.
 18. The non-transitory computer-readable medium ofclaim 16, wherein the instructions, when executed by the processor,further cause the processor to provide a recommendation to an operatorto adjust a pump rate, a surface pressure, or a proppant volume toachieve a particular net pressure gain following the event.
 19. Thenon-transitory computer-readable medium of claim 16, wherein theinstructions, when executed by the processor, further cause theprocessor to automatically adjust a pump rate, a surface pressure, aproppant volume, or combinations thereof responsive to the event. 20.The non-transitory computer-readable medium of claim 16, wherein a timeassociated with the corresponding simulated net pressure value isapproximately the same as a time associated with the actual net pressurevalue.